# Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

arXiv: https://arxiv.org/abs/2412.01459 · HTML: https://mlsanity.com/paper/2412.01459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 124 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Thousands of AI Authors on the Future of AI](https://mlsanity.com/paper/2401.02843/) (2024)
- [Long-Term Trends in the Public Perception of Artificial Intelligence](https://mlsanity.com/paper/1609.04904/) (2016)
- [Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countries](https://mlsanity.com/paper/2001.00081/) (2019)
- [The Impact of Large Language Models in Academia: from Writing to Speaking](https://mlsanity.com/paper/2409.13686/) (2024)
- [Mapping acceptance: micro scenarios as a dual-perspective approach for assessing public opinion and individual differences in technology perception](https://mlsanity.com/paper/2402.01551/) (2024)
- [Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance](https://mlsanity.com/paper/2411.19356/) (2024)
- [The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success](https://mlsanity.com/paper/2402.14728/) (2024)
- [Laypeople and Experts risk perception of Cloud Computing Services](https://mlsanity.com/paper/1509.06536/) (2015)
- [The Problem of Alignment](https://mlsanity.com/paper/2401.00210/) (2023)

## Abstract (arXiv)

Artificial Intelligence (AI) is reshaping many societal domains, raising critical questions about its risks, benefits, and the potential misalignment between public and academic perspectives. This study examines how the general public (N=1110) -- individuals who interact with or are impacted by AI technologies -- and academic AI experts (N=119) -- those elites shaping AI development -- perceive AI's capabilities and impact across 71 scenarios. These scenarios span domains such as sustainability, healthcare, job performance, societal inequality, art, and warfare. Participants evaluated these scenarios across four dimensions using the psychometric model: likelihood, perceived risk and benefit, and overall value (or sentiment). The results suggest significant differences: experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts. Moreover, both groups apply different weighting schemes: experts discount risk more heavily relative to benefit than non-experts. Visual mappings of these evaluations uncover areas convergent evaluations (e.g., AI performing medical diagnoses or criminal use) as well as tension points (e.g., decision of legal cases, political decision making), highlighting areas where communication and policy interventions may be needed. These findings underscore a critical translational challenge: if AI research and deployment are to align with societal priorities, the perception gap between developers and the public must be better understood and addressed. Our results provide an empirical foundation for value-sensitive AI governance and trust-building strategies across stakeholder groups.